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Teknik Etkinsizlik Kaynaklı Üretim Kayıpları: BRICS-T Ülkeleri Örneğinde Bir Panel Veri Analizi

Yıl 2023, , 53 - 73, 28.07.2023
https://doi.org/10.26650/ekoist.2023.38.1116692

Öz

Çalışmada, BRICS-T ülkelerinin mevcut kaynaklarını ve teknolojiyi kullanma etkinliğinin tahmin edilmesi amaçlanmaktadır. Bu kapsamda, 1990-2019 dönemi ve 6 ülkeden oluşan bir panel veri seti ile stokastik sınır analizi (SSA) kullanılarak üretim sınırı modellenmektedir. Üretim sınırı ve teknik etkinsizlik belirleyicilerinin tahmini için tek aşamalı bir yöntem kullanılmaktadır. Ayrıca, modelde teknik etkinsizlik ile teknik etkinsizliğe ve istatistiki hata terimine ilişkin varyanslar, ülke nüfusu ve ihracatın ithalatı karşılama oranı değişkenlerinin bir fonksiyonu olarak tanımlanmaktadır. Çalışmada, söz konusu değişkenlerin, teknik etkinsizlik üzerinde anlamlı bir etkisi olmadığı tespit edilmektedir. Bulgular, analiz döneminde teknik etkinlik düzeyinin ortalama %91 olduğunu ve ülkelerin bu dönemde potansiyel çıktılarının %9’unu kaybettiğini göstermektedir. Analiz döneminde ortalama teknik etkinlik katsayısı açısından ilk sırada yer alan ülke Türkiye, son sırada yer alan ülke ise Rusya olarak tespit edilmiştir. Türkiye’nin etkinsizlik kaynaklı üretim kaybı %3,4 iken Rusya’nın %23,3’tür. Ayrıca Rusya analiz dönemi süresince etkinliğini artıran tek ülke olarak tespit edilmiştir. Diğer yandan analiz döneminde, yıllık ortalama olarak teknik etkinlik %0,064 azalış göstermiştir. Bu bulgu, ülkelerin mevcut teknolojilere uyumlarının giderek azaldığına ve etkinsizlik kaynaklı üretim kayıplarının arttığına işaret etmektedir. Gerek etkinsizlik kaynaklı üretim kayıpları gerekse analiz döneminde gözlemlenen etkinlik düşüşleri, sürdürülebilir ekonomik büyüme açısından BRICS-T ülkelerinin potansiyellerini daha etkin kullanmalarının önemli bir fırsat alanı olduğunu göstermektedir.

Kaynakça

  • Aguiar, D. I. R. (2014). Measuring the Differences in Productivities of Nations: A Stochastic Frontier Approach. Doctoral dissertation. Tese de Mestrado, Porto: Universidade Católica Portuguesa. google scholar
  • Akhremenko, A., Petrov, A., and Yureskul, E. (2019). Institutions, Productivity Change, and Growth. In S. Smirnov, A. Ozyildirim, and P. Picchetti (eds.), Business Cycles in BRICS (pp. 29-54). Berlin-Heidelberg: Springer. google scholar
  • Albert, M. G. (1998). Regional Technical Efficiency: A Stochastic Frontier Approach. Applied Economics Letters, 5(11), 723-726. google scholar
  • Battese, G. E., and Coelli, T. J. (1988). Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data. Journal of Econometrics, 38(3), 387-399. google scholar
  • Battese, G. E., and Coelli, T. J. (1992). Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India. Journal of Productivity Analysis, 3(1), 153-169. google scholar
  • Batttese, G. E., and Coelli, T. J. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics, 20(2), 325-332. google scholar
  • Belotti, F., and Ilardi, G. (2018). Consistent Inference in Fixed-Effects Stochastic Frontier Models. Journal of Econometrics, 202(2), 161-177. google scholar
  • Belotti, F., Daidone, S., Ilardi, G., and Atella, V. (2013). Stochastic Frontier Analysis Using Stata. The Stata Journal, 13(4), 719-758. google scholar
  • Caselli, F. (2005). Accounting for Cross-Country Income Differences. Handbook of Economic Growth, 1, 679-741. google scholar
  • Chen, Y.-Y., Schmidt, P., and Wang, H.-J. (2014), Consistent Estimation of the Fixed Effects Stochastic Frontier Model, Journal of Econometrics, 18(2), 65-76. google scholar
  • Deliktaş, E., and Balcılar, M. (2005). A Comparative Analysis of Productivity Growth, Catch-Up, and Convergence in Transition Economies. Emerging Markets Finance and Trade, 41(1), 6-28. google scholar
  • Du, K. (2017). Translog: Stata Module to Create New Variables for a Translog Function, Statistical Software Components S458318, Boston College Department of Economics. Link: https://ideas. repec.org/c/boc/bocode/s458318.html google scholar
  • Farrell, M. J. (1957), The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120, 253-90. google scholar
  • Forstner, H., and Isaksson, A. (2002). Productivity, Technology, and Efficiency: An Analysis of the World Technology Frontier; When Memory is Infinite. Statistics and Information Networks Branch of UNIDO. google scholar
  • Golany, B., and Thore, S. (1997). The Economic and Social Performance of Nations: Efficiency and Returns to Scale. Socio-Economic Planning Sciences, 31(3), 191-294. google scholar
  • Greene, W. (2005a). Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23(1), 7-32. google scholar
  • Greene, W. (2005b). Reconsidering Heterogeneity in Panel Data Estimators of The Stochastic Frontier Model. Journal ofEconometrics, 126(2). 269-303. google scholar
  • Hall, R. E., and Jones, C. I. (1999). Why Do Some Countries Produce so Much More Output per Worker than Others?. The Quarterly Journal ofEconomics, 114(1), 83-116. google scholar
  • Herrera, S., and Pang, G. (2005). Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach (Vol. 3645). World Bank Publications. google scholar
  • Heshmati, A., and Rashidghalam, M. (2020). Estimation of Technical Change and TFP Growth Based on Observable Technology Shifters. Journal ofProductivity Analysis, 53, 21-36. google scholar
  • Hou, Z., Roseta-Palma, C., and Ramalho, J. J. (2020). Directed Technological Change, Energy and More: A Modern Story. Environment and Development Economics, 25(6). 611-633. google scholar
  • Human Capital in PWT 9.0. (n.d.). [ebook] Penn World Table. Available at: http://www.rug.nl/ ggdc/docs/human_capital_in_pwt_90.pdf [Erişim: 27/09/2020]. google scholar
  • Kim, S., and Lee, H. (2006). The Productivity Debate of East Asia Revisited: A Stochastic Frontier Approach. Applied Economics, 38, 1697-1706. google scholar
  • Kim, S., Park, D., and Park, J.-H. (2010). Productivity Growth Across the World, 1991-2003. Asian Development Bank Economics Working Paper Series (212). google scholar
  • Kodde, D. A., and Palm, F. C. (1986). Wald Criteria for Jointly Testing Equality and Inequality Restrictions. Econometrica: Journal of the Econometric Society, 1243-1248. google scholar
  • Kök, R., and Deliktaş , E. (2003). Endüstri İktisadında Verimlilik Ölçme ve Strateji Geliştirme Teknikleri. İzmir: DEÜ İİBF Yayınları, Yayın Karar No.25-8/1. google scholar
  • Krüger , J., Cantner, U., and Hanusch, H. (2000). Total Factor Productivity, the East Asian Miracle, and the World Production Frontier. Weltwirtschaftliches Archiv, 136(1), 111-136. google scholar
  • Kumbhakar, S. C. (1990). Production Frontiers, Panel Data and Time-Varying Technical Inefficiency. Journal of Econometrics, 46(1-2), 201-211. google scholar
  • Kumbhakar, S. C., and Heshmati, A. (1995). Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976-88. American Journal of Agricultural Economics, 77(3), 660-674. google scholar
  • Kumbhakar, S. C., Lien, G., and Hardaker, J. B. (2014). Technical Efficiency in Competing Panel Data Models: A Study of Norwegian Grain Farming. Journal of Productivity Analysis, 41(2), 321-337. google scholar
  • Kumbhakar, S. C., Wang, H.-J., and Horncastle, A. P. (2015). A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata. New York: Cambridge University Press. google scholar
  • Kumbhakar, S., and Lovell, C. (2000). Stochastic Frontier Analysis. New York: Cambridge University Press. google scholar
  • Mahadevan, R. (2004). The Economics of Productivity in Asia and Australia. Massachusetts: Edward Elgar Publishing. google scholar
  • Penn World Table. (2021). Groningen Growth and Development Centre, Faculty of Economics and Business. Web: https://www.rug.nl/ggdc/productivity/pwt/?lang=en [Erişim: 24.03.2022]. google scholar
  • Pires , J., and Garcia, F. (2012). Productivity of Nations: A Stochastic Frontier Approach to TFP Decomposition. Economics Research International (Article ID 584869), 1-20. google scholar
  • Rao, D.S.P. and Coelli, T.J. (1998). A Cross-Country Analysis of GDP Growth Catch-Up And Convergence in Productivity and Inequality, Centre for Efficiency and Productivity Analysis (CEPA). Working Paper No. 5/98, University of New England, Australia. google scholar
  • Wang, H. J. (2002). Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model. Journal of Productivity Analysis, 18(3), 241-253. google scholar
  • Wang, H.-J., and Ho, C.-W. (2010). Estimating Fixed-Effect Panel Stochastic Frontier Models by Model Transformation. Journal of Econometrics, 157(2), 286-296. google scholar

Production Losses Due to Technical Inefficiency: A Panel Data Analysis on the Case of BRICS-T Countries

Yıl 2023, , 53 - 73, 28.07.2023
https://doi.org/10.26650/ekoist.2023.38.1116692

Öz

This study aims to estimate the efficiency of using the available resources and technology of BRICS-T countries. In this context, the production limit is modelled using stochastic frontier analysis (SFA) within the scope of a panel data set consisting of six countries from 1990-2019. The production frontier and technical inefficiency determinants are estimated by a one-stage method. In addition, the technical inefficiency in the model and the variances related to technical inefficiency and statistical error are defined as a function of the variables of countries’ population and export-import ratios. The study has determined these variables to have no significant impact on technical inefficiency. The results show the average level of technical efficiency during the analysis period to be 91% and countries to have lost 9% of their potential output. Turkey was determined as the country to rank first and Russia to rank last during the analyzed period in terms of the average coefficient of technical efficiency. Turkey’s production loss due to inefficiency was 3.4%, while Russia’s was 23.3%. On the other hand, technical efficiency decreased an average of 0.064% annually during the analysis period. This finding indicates countries’ adaptation to existing technologies to gradually decrease and production losses due to inefficiency to increase. The production losses due to inefficiency and the efficiency decreases observed during the analysis period reveal an important opportunity for BRICS-T countries to use their potential more effectively in terms of sustainable economic growth.

Kaynakça

  • Aguiar, D. I. R. (2014). Measuring the Differences in Productivities of Nations: A Stochastic Frontier Approach. Doctoral dissertation. Tese de Mestrado, Porto: Universidade Católica Portuguesa. google scholar
  • Akhremenko, A., Petrov, A., and Yureskul, E. (2019). Institutions, Productivity Change, and Growth. In S. Smirnov, A. Ozyildirim, and P. Picchetti (eds.), Business Cycles in BRICS (pp. 29-54). Berlin-Heidelberg: Springer. google scholar
  • Albert, M. G. (1998). Regional Technical Efficiency: A Stochastic Frontier Approach. Applied Economics Letters, 5(11), 723-726. google scholar
  • Battese, G. E., and Coelli, T. J. (1988). Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data. Journal of Econometrics, 38(3), 387-399. google scholar
  • Battese, G. E., and Coelli, T. J. (1992). Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India. Journal of Productivity Analysis, 3(1), 153-169. google scholar
  • Batttese, G. E., and Coelli, T. J. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics, 20(2), 325-332. google scholar
  • Belotti, F., and Ilardi, G. (2018). Consistent Inference in Fixed-Effects Stochastic Frontier Models. Journal of Econometrics, 202(2), 161-177. google scholar
  • Belotti, F., Daidone, S., Ilardi, G., and Atella, V. (2013). Stochastic Frontier Analysis Using Stata. The Stata Journal, 13(4), 719-758. google scholar
  • Caselli, F. (2005). Accounting for Cross-Country Income Differences. Handbook of Economic Growth, 1, 679-741. google scholar
  • Chen, Y.-Y., Schmidt, P., and Wang, H.-J. (2014), Consistent Estimation of the Fixed Effects Stochastic Frontier Model, Journal of Econometrics, 18(2), 65-76. google scholar
  • Deliktaş, E., and Balcılar, M. (2005). A Comparative Analysis of Productivity Growth, Catch-Up, and Convergence in Transition Economies. Emerging Markets Finance and Trade, 41(1), 6-28. google scholar
  • Du, K. (2017). Translog: Stata Module to Create New Variables for a Translog Function, Statistical Software Components S458318, Boston College Department of Economics. Link: https://ideas. repec.org/c/boc/bocode/s458318.html google scholar
  • Farrell, M. J. (1957), The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120, 253-90. google scholar
  • Forstner, H., and Isaksson, A. (2002). Productivity, Technology, and Efficiency: An Analysis of the World Technology Frontier; When Memory is Infinite. Statistics and Information Networks Branch of UNIDO. google scholar
  • Golany, B., and Thore, S. (1997). The Economic and Social Performance of Nations: Efficiency and Returns to Scale. Socio-Economic Planning Sciences, 31(3), 191-294. google scholar
  • Greene, W. (2005a). Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity Analysis, 23(1), 7-32. google scholar
  • Greene, W. (2005b). Reconsidering Heterogeneity in Panel Data Estimators of The Stochastic Frontier Model. Journal ofEconometrics, 126(2). 269-303. google scholar
  • Hall, R. E., and Jones, C. I. (1999). Why Do Some Countries Produce so Much More Output per Worker than Others?. The Quarterly Journal ofEconomics, 114(1), 83-116. google scholar
  • Herrera, S., and Pang, G. (2005). Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach (Vol. 3645). World Bank Publications. google scholar
  • Heshmati, A., and Rashidghalam, M. (2020). Estimation of Technical Change and TFP Growth Based on Observable Technology Shifters. Journal ofProductivity Analysis, 53, 21-36. google scholar
  • Hou, Z., Roseta-Palma, C., and Ramalho, J. J. (2020). Directed Technological Change, Energy and More: A Modern Story. Environment and Development Economics, 25(6). 611-633. google scholar
  • Human Capital in PWT 9.0. (n.d.). [ebook] Penn World Table. Available at: http://www.rug.nl/ ggdc/docs/human_capital_in_pwt_90.pdf [Erişim: 27/09/2020]. google scholar
  • Kim, S., and Lee, H. (2006). The Productivity Debate of East Asia Revisited: A Stochastic Frontier Approach. Applied Economics, 38, 1697-1706. google scholar
  • Kim, S., Park, D., and Park, J.-H. (2010). Productivity Growth Across the World, 1991-2003. Asian Development Bank Economics Working Paper Series (212). google scholar
  • Kodde, D. A., and Palm, F. C. (1986). Wald Criteria for Jointly Testing Equality and Inequality Restrictions. Econometrica: Journal of the Econometric Society, 1243-1248. google scholar
  • Kök, R., and Deliktaş , E. (2003). Endüstri İktisadında Verimlilik Ölçme ve Strateji Geliştirme Teknikleri. İzmir: DEÜ İİBF Yayınları, Yayın Karar No.25-8/1. google scholar
  • Krüger , J., Cantner, U., and Hanusch, H. (2000). Total Factor Productivity, the East Asian Miracle, and the World Production Frontier. Weltwirtschaftliches Archiv, 136(1), 111-136. google scholar
  • Kumbhakar, S. C. (1990). Production Frontiers, Panel Data and Time-Varying Technical Inefficiency. Journal of Econometrics, 46(1-2), 201-211. google scholar
  • Kumbhakar, S. C., and Heshmati, A. (1995). Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976-88. American Journal of Agricultural Economics, 77(3), 660-674. google scholar
  • Kumbhakar, S. C., Lien, G., and Hardaker, J. B. (2014). Technical Efficiency in Competing Panel Data Models: A Study of Norwegian Grain Farming. Journal of Productivity Analysis, 41(2), 321-337. google scholar
  • Kumbhakar, S. C., Wang, H.-J., and Horncastle, A. P. (2015). A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata. New York: Cambridge University Press. google scholar
  • Kumbhakar, S., and Lovell, C. (2000). Stochastic Frontier Analysis. New York: Cambridge University Press. google scholar
  • Mahadevan, R. (2004). The Economics of Productivity in Asia and Australia. Massachusetts: Edward Elgar Publishing. google scholar
  • Penn World Table. (2021). Groningen Growth and Development Centre, Faculty of Economics and Business. Web: https://www.rug.nl/ggdc/productivity/pwt/?lang=en [Erişim: 24.03.2022]. google scholar
  • Pires , J., and Garcia, F. (2012). Productivity of Nations: A Stochastic Frontier Approach to TFP Decomposition. Economics Research International (Article ID 584869), 1-20. google scholar
  • Rao, D.S.P. and Coelli, T.J. (1998). A Cross-Country Analysis of GDP Growth Catch-Up And Convergence in Productivity and Inequality, Centre for Efficiency and Productivity Analysis (CEPA). Working Paper No. 5/98, University of New England, Australia. google scholar
  • Wang, H. J. (2002). Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model. Journal of Productivity Analysis, 18(3), 241-253. google scholar
  • Wang, H.-J., and Ho, C.-W. (2010). Estimating Fixed-Effect Panel Stochastic Frontier Models by Model Transformation. Journal of Econometrics, 157(2), 286-296. google scholar
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonometri (Diğer)
Bölüm ARAŞTIRMA MAKALESI
Yazarlar

Nadide Yiğiteli 0000-0002-0632-7253

Yayımlanma Tarihi 28 Temmuz 2023
Gönderilme Tarihi 14 Mayıs 2022
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yiğiteli, N. (2023). Teknik Etkinsizlik Kaynaklı Üretim Kayıpları: BRICS-T Ülkeleri Örneğinde Bir Panel Veri Analizi. EKOIST Journal of Econometrics and Statistics(38), 53-73. https://doi.org/10.26650/ekoist.2023.38.1116692